This package is primarily to provide data that is more similar to what many people would typically come across in the wild, or is simply more interesting or accessible (in my opinion), and more useful for instruction and workshops. Far too often examples use iris, mtcars, etc. for convenience, but these are actually inconvenient for demonstrating common data and modeling problems, or are too small to even be realistic.

This package will provide larger and messier data. The bias is towards data that could be understood regardless of discipline/background. In addition, it should have minimally several hundred observations, and often much larger, but not so large that analysis or data processing demonstration would take an inordinate amount of time. However, it should have relatively few columns (unless for demonstration of a ‘large p’ type of problem/analysis, e.g. penalized regression.).

In general the goals are:

• Large(-ish) data. At least enough to make analysis and visualization interesting.
• Clean messy data (not a typo). Most of the data is relatively clean, but some will have missing values, require text cleaning, etc.
• Well documented data. All columns will have descriptions (unless otherwise not possible), and article references and web sources will be provided.
• Well named data. It can be very annoying if something is called d, an author name, an acronym, etc. Columns are also renamed for some data sets for clarity.
• Data that covers common analyses.

In most cases the data has been cleaned up to make it easier to use and understand.

Right now it has:

• gapminder_2019: a 2019 pull from gapminder.org/data.
  • A nice longitudinal/time-series data set suitable for a wide range of standard and more complex mixed models, spatial visualization and analyses, etc.
• star_wars: several data sets based on the Star Wars API.
  • Mostly just for fun, but it demonstrates usage of list columns and otherwise could be good for demonstrating joins.
• instructor_evaluations: a nice-sized data set for mixed/multi-level modeling taken from the lme4 package.
  • Good for mixed models and similar.
• fish: Number of fish caught on camping trips.
  • An accessible data set useful for demonstrating count models including zero-inflated/hurdle models.
• pisa: OECD’s Programme for International Student Assessment with international scores for math, science, and reading, covering years 2000-2015.
  • Potentially good for demonstrating nonlinear relationships (e.g. GAM), imputing missing data, longitudinal/spatial analyses.
• world_happiness: Multiyear data set with country level scores of ‘happiness’. From 2019 World Happiness Report, and includes data from 2005-2018.
  • Similar to Gapminder and PISA, this could be used for a variety of standard statistical models.
• sp500: Daily S & P 500 data for a 10 year period covering +- 5 years before and after the Great Recession low.
  • Good for time series and related analyses. Includes industry classifications.
• wine_reviews, wine_quality: Two data sets regarding wine reviews that can be used for a wide range of standard statistical and machine learning.
  • Can be used for standard regression and classification, ordinal regression, text analysis, sentiment analysis.
• google_apps: Ratings and other information for Google Play Store apps.
  • Text & sentiment analysis, standard regression, etc.
• fashion_train, fasion_test: The ‘Fashion MNIST’. Image data for clothing items.
  • Image classification, cluster analysis
• gender_gap, gender_gap_2018: Country level data regarding the World Bank Gender Gap Index.
  • Longitudinal analysis, geospatial analysis, etc.
• kiva: Lending information from kiva.org online crowdfunding platform.
  • econometric, geospatial, multilevel, etc.
• water_risk, water_risk_province: Country and province level data regarding water risk.
  • geospatial analysis and visualization
• big_five: Big Five personality traits.
  • scale reliability, factor analysis, item response theory, structural equation modeling.
• heart_disease: The UCI heart disease data.
  • survival, classification.
• retirement: Data on retirement plan participation rate of employees.
  • binomial glm, fractional regression, beta regression.
• movielens: 1 million samples from MovieLens data.
  • recommendation systems, item response theory, cluster analysis, etc.

This package is not on CRAN. To install:

devtools::install_github('m-clark/noiris')

To do:

• Data for basic classification
• Data for basic machine learning (regression and classification)
• Data for text analysis (more to come)
• Data for image classification
• Data for survival analysis
• Data for factor analysis/SEM (PCA?)
• Data on the unit interval suitable for binomial, beta regression, etc.
• Data for non-obvious cluster analysis (no iris! and no old faithful either!).
• Data for network/graphical models and visualization.

Note to self, see flexmix, poLCA, and other packages. Maybe add classic biochemists for another count data set. Article pub for link models and related.